Digital image processing (DIP), also called computer image processing, refers to a process of converting an image signal into a digital signal and processing the digital signal with computer. By processing an image with DIP technology, useful information can be acquired to the maximum extent. However, due to impacts of factors such as lighting, environments, and equipment, an image may have a shadowy part and a highlighted part. Adjustment on the shadowy part and the highlighted part may directly affect visual perception of human during observation and quality of the image.
Automatic partial adjustment on shadow and highlight of an image is mainly performed with a histogram-based method, for example, the histogram equalization, i.e., performing nonlinear extension on an image, and reallocating pixel values of the image to make the number of pixels in a certain grayscale range approximately the same and change histogram distribution of the given image into “even” histogram distribution.
Brightness information of an image is adjusted by using the histogram-based method. Because histogram equalization is indiscriminate in processed image data, contrast of background noise may be increased and contrast of useful signals may be reduced. Moreover, a case of partial discontinuity is apt to occur in a processed image, resulting in loss of detailed information in the image.